#!/usr/bin/env python
# coding: utf-8

# # pybacktest tutorial
# 
# This tutorial will give you a quick overview of **pybacktest**'s features. To do so, we will backtest classic MA crossover trading strategy:
# 
# * go long when short MA crosses above long MA
# * go short when short MA crosses below long MA
# * entry rules are also exit rules, thus making strategy reversible
# 
# Package is available for download at https://github.com/ematvey/pybacktest

# In[1]:


from __future__ import print_function

import pybacktest  # obviously, you should install pybacktest before importing it
import pandas as pd


# **pybacktest** expects bars to be in `pandas.DataFrame` indexed by datetimestamps, with columns named `O`, `H`, `L`, `C`. Actually, in this version it would only check `O`, and only if trade prices are not explicitly specified.
# 
# Lets load data from yahoo using helper.

# In[2]:


ohlc = pybacktest.load_from_yahoo('SPY')
ohlc.tail()


# Now time to define strategy. To do so, all we need to do is to create binary `Series` with signals, and, optionally, trade price `Series` with float elements.
# 
# Could this be any simpler?

# In[3]:


short_ma = 50
long_ma = 200

ms = ohlc.C.rolling(short_ma).mean()
ml = ohlc.C.rolling(long_ma).mean()
    
buy = cover = (ms > ml) & (ms.shift() < ml.shift())  # ma cross up
sell = short = (ms < ml) & (ms.shift() > ml.shift())  # ma cross down

print('>  Short MA\n%s\n' % ms.tail())
print('>  Long MA\n%s\n' % ml.tail())
print('>  Buy/Cover signals\n%s\n' % buy.tail())
print('>  Short/Sell signals\n%s\n' % sell.tail())


# Time to run backtest. `Backtest` will try to extract signals and prices and bars from whatever dict-like object you passed as first argument. Could be dict, could be pandas.DataFrame or anything.
# 
# To make thing easier, pass local namespace (extracted py calling `locals()`), that contains every variable you created up to this point.

# In[4]:


bt = pybacktest.Backtest(locals(), 'ma_cross')


# `Backtest` runs lazily, i.e. it calculates anything only when you call properties from it. There properties: 

# In[5]:


print(list(filter(lambda x: not x.startswith('_'), dir(bt))))
print('\n>  bt.signals\n%s' % bt.signals.tail())
print('\n>  bt.trades\n%s' % bt.trades.tail())
print('\n>  bt.positions\n%s' % bt.positions.tail())
print('\n>  bt.equity\n%s' % bt.equity.tail())
print('\n>  bt.trade_price\n%s' % bt.trade_price.tail())


# Some of popular performance statistics could be requested by calling `summary` method of `Backtest`.

# In[6]:


bt.summary()


# Now lets look at equity curve.

# In[7]:


get_ipython().run_line_magic('matplotlib', 'inline')
import matplotlib
import matplotlib.pyplot as plt
matplotlib.rcParams['figure.figsize'] = (15.0, 8.0)

bt.plot_equity()


# But what if you want to see what exactly was going on during backtest run? Well, `Backtest` can plot trades as they happened for you. Legend is hidden by default to save space.

# In[8]:


bt.plot_trades()
ohlc.C.rolling(short_ma).mean().plot(c='green')
ohlc.C.rolling(long_ma).mean().plot(c='blue')
plt.legend(loc='upper left')
pass


# Can you see anything there? I can't. That's why we have special a very special `trdplot` property, which allowes you specify what period do you want to plot using standard `pandas` indexing mechanisms. Same trick could be done with equity curve using `eqplot` property.

# In[9]:


bt.trdplot['2004':'2007']
ohlc.C['2004':'2007'].rolling(short_ma).mean().plot(c='green')
ohlc.C['2004':'2007'].rolling(long_ma).mean().plot(c='blue')
pass


# That's the most of it.